Determination of four priority polycyclic aromatic hydrocarbons (4PAHs) by GC-MS in traditional Turkish yoghurts

ABSTRACT

Polycyclic aromatic hydrocarbons (PAHs) are ingested with foods and cause toxicities for humans. Yoghurt is one of the main foods that mediate PAH ingestion. Yan?k yoghurt, a traditional strained yoghurt, can contain PAHs due to its production process. Therefore, the concentration and types of four polycyclic aromatic hydrocarbons were determined by gas chromatography-mass spectrometry (GC-MS). Yan?k yoghurt samples were collected from different shops in Denizli, Turkey. For the method validation, the Eurachem guide (second edition) was adopted. Accordingly, linearity, working range, limits of detection (LOD), quantification (LOQ), precision and trueness values were set. Due to the selected validation parameters, the limit for detection was between 0.03 ng g^?1and 0.05 ng g^?1while the limit for quantification was between 0.10 ng g^?1 and 0.16 ng g^?1. Recovery rates higher than 83.80 % were obtained for four PAHs at two spiked concentrations (2.0 and 5.0 ng g^?1). Five Yan?k yoghurt samples and one commercial concentrated yoghurt sample were successfully analysed using the validated method.     

基于线特征及迭代最近点算法的地基建筑物点云自动配准方法

为克服迭代最近点(ICP)算法易陷入局部最优的缺陷，提出一种基于线特征及ICP算法的地基建筑物点云自动配准方法。首先，基于法向一致性进行建筑物点云平面分割；接着，采用alpha-shape算法进行点簇轮廓线提取，并拆分和拟合处理得到特征线段；然后，以线对作为配准基元，以线对夹角和距离作为相似性测度进行同名特征匹配，实现建筑物点云的粗配准；最后，以粗配准结果为初值，进一步采用ICP算法完成点云精确配准。利用两组部分重叠的建筑物点云进行配准实验，实验结果表明，采用由粗到精的配准方法能有效改善ICP算法对初值依赖的问题，实现具有部分重叠的建筑物点云的有效配准。     

Gelation properties of goose liver protein recovered by isoelectric solubilisation/precipitation process

Isoelectric solubilisation/precipitation (ISP) process was applied to goose liver (GL) for protein extraction. The gelation properties of proteins extracted by acid processes (ACP, pH 2.0, 2.5 and 3.0) and alkaline processes (ALP, pH 11.0, 11.5 and 12.0) were estimated, where the unextracted ground GL was set as the control. Nearly 58.39~79.00% of GL proteins were recovered by ISP treatments. High molecular weight (100~250 kDa) proteins were found to be partially hydrolysed by ACP, while few changes in proteins occurred during ALP. As evidenced by rheological and textural measurements, ALP proteins formed gels with high elasticity and superior texture, whereas ACP proteins had inferior gelation properties. Moreover, ALP proteins were able to form a highly interconnected and homogeneous three‐dimensional microstructure. Predominantly, gels produced by 11.0 had optimal texture and the lowest cooking loss (P < 0.05). These results suggested that the ISP process (ALP) is a potential method to improve the economic value of GL.     

瓦斯抽采集成旁通阀二位放水器研制

针对井下瓦斯抽采中原放水器配气阀安装不规范、双截止阀使用繁琐、工人劳动强度大等现状,研制了具有集成调控装置的放水器,能够实现一个阀门控制所有气路和水路,自主放水和负压排水,从源头上杜绝了瓦斯喷孔和水流溢出等情况,大大降低了职工的劳动强度,实现了井下抽采系统安全可靠运行。     

Nonlinear Factor-Augmented Predictive Regression Models with Functional Coefficients

This article introduces a new class of functional-coefficient predictive regression models, where the regressors consist of auto-regressors and latent factor regressors, and the coefficients vary with certain index variable. The unobservable factor regressors are estimated through imposing an approximate factor model on high dimensional exogenous variables and subsequently implementing the classical principal component analysis. With the estimated factor regressors, a local linear smoothing method is used to estimate the coefficient functions (with appropriate rotation) and obtain a one-step ahead nonlinear forecast of the response variable, and then a wild bootstrap procedure is introduced to construct the prediction interval. Under regularity conditions, the asymptotic properties of the proposed methods are derived, showing that the local linear estimator and the nonlinear forecast using the estimated factor regressors are asymptotically equivalent to those using the true latent factor regressors. The developed model and methodology are further generalized to the factor-augmented vector predictive regression with functional coefficients. Finally, some extensive simulation studies and an empirical application to forecast the UK inflation are given to examine the finite-sample performance of the proposed model and methodology.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

基于词法特征的恶意域名快速检测算法

针对互联网中恶意域名攻击事件频发，现有域名检测方法实时性不强的问题，提出一种基于词法特征的恶意域名快速检测算法。该算法根据恶意域名的特点，首先将所有待测域名按照长度进行正则化处理后赋予权值；然后利用聚类算法将待测域名划分成多个小组，并利用改进的堆排序算法按照组内权值总和计算各域名小组优先级，根据优先级降序依次计算各域名小组中每一域名与黑名单上域名之间的编辑距离；最后依据编辑距离值快速判定恶意域名。算法运行结果表明，基于词法特征的恶意域名快速检测算法与单一使用域名语义和单一使用域名词法的恶意域名检测算法相比，准确率分别提高1.7%与2.5%，检测速率分别提高13.9%与6.8%，具有更高的准确率和实时性。     

鲸鱼优化支持向量机的短期风电功率预测

为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。     

GC-MS法测定塑胶跑道面层中的6种有害物质

建立了检测塑胶跑道面层中6种有害物质(环己基异氰酸酯(CHI)、2,4-甲苯二异氰酸酯(2,4-TDI)、2,6-甲苯二异氰酸酯(2,6-TDI)、六亚甲基二异氰酸酯(HDI)、萘-1,5-二异氰酸酯(NDI)和4,4'-二苯基甲烷二异氰酸酯(4,4'-MDI))含量的GC-MS检测方法,分别讨论了定量方式和萃取时间对6种有害物质回收率的影响。将样品剪碎至10~20目(0.85~2.00 mm)的颗粒,用10 mL乙酸乙酯萃取,室温下超声20 min,提取液用DB-17MS石英毛细管色谱柱(30 m×0.25 mm×0.25μm)分离,采用GC-MS法并选择离子检测,内标法定量。结果表明,在0.5~100.0 mg/L(4,4'-MDI在1.0~100.0 mg/L)浓度范围内,线性良好,相关系数均大于0.998,方法检出限范围为2.0~10.0 mg/kg,定量限范围为6.0~20.0 mg/kg;加标平均回收率在80.1%~100.8%之间,相对标准偏差(n=6)小于5.2%。该方法快速、准确,适用于塑胶跑道面层中有害物质的测定。